The big theme of the last year has been big data. There was a lot of innovation in many areas, but big data has had a huge impact on both how organizations plan their overall technology strategy as well as affecting other specific strategies such as analytics, cloud, mobile, social, and collaboration.

Steve kicked off by addressing the confusion (and cynicism) about the definition of “big data” — noting that people had supplied at least twenty different definitions in response to his question on Twitter. The popularity of the term has been driven by the rise of new open-source technology technology such as Hadoop, but it is now typically used to refer to what Gartner calls “extreme data”.

Extreme data is on the high end of one or more of the ‘3Vs’: Volume, Velocity, and Variety (and some note that there’s a fourth V, validity, that must be taken account of: data quality remains the #1 struggle for organizations trying to implement successful analytic projects).

To address all of these effectively, any “big data solution” has to encompass a wide range of different technologies. SAP is proposing a new “Big Data Processing Framework” that includes integration to new tools such as Hadoop, but also addresses the need for the other ‘V’s for a global approach to ingesting, storing, processing, and presenting data from both structured and less-structured sources. Many more details about this framework will be available in the coming months. (emphasis added)

Twenty different definitions of “big data?” No wonder I have been confused. Well, that’s one explanation anyway.

There is another confusion, one that Timo the promotion of SAP solutions doesn’t address.

That confusion is whether “big data,” by any definition, is relevant for a project and/or enterprise.

Digital data is doubly every eighteen months, but that doesn’t mean that every project has to cope with the four V’s (Volume, Velocity, Variety, Validity).

Rather, every project has to cope with relevant big data and the relevant four V’s.

Unless and until you specify your RBD and RV4, you can’t meaningfully evaluate the solutions offered by SAP or anyone else for “big data.”

Their products work for their vision of “big data.”

Your project needs to work for your vision of “big data.”

Now there is a topic map project that the Economist or some similar group could undertake. Create a topic map to cut across the product hype around applications to deal with “big data” so consumers (even enterprises) can find products for their relevant big data.

This entry was posted
on Friday, March 2nd, 2012 at 8:04 pm and is filed under BigData, Marketing.
You can follow any responses to this entry through the RSS 2.0 feed.
Both comments and pings are currently closed.

Information services (editing/standards/development) are what I sell so I understand the conflict between advancing the field and retaining value-add for customers.

On the other hand, information management is advancing so rapidly, witness the rise of Hadoop, being closely chased by graph databases, that management projections may make mid-level managers feel involved, but only just.

I think Gartner’s position, should it choose to take the lead, could standardize some of the vocabulary in the area. Not all, but some.

Say a quarterly vocabulary list of terminology. Would benefit others, but then customers would remember who took the lead as well.